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A leave-one epoch -out (leave-one epoch -outn paradigm was used for cleave-one epoch -out testing.
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a, Test patch of skin used for classifier testing.
A fuzzy clustering algorithm was used for the classifier design.
Since SVM and AdaBoost were originally designed for binary classifier, in this paper, OAO (One Against one) approach was used for classifiers to generate a multi-classifier to train the model.
Based on Rand calculation, a statistical method was used to pick out the data points (genes) for classifier design.
For classifier design, the developed fuzzy classifier was compared with the well known BPNN classifier frequently used in other studies.
The training set, where the prognosis of each case was known, was used for designing classifiers based on single texture features, whereas the independent validation set was used for evaluation of the classifiers.
The training set of 175 patients was used for designing classifiers based on single texture features.
In the filter approach, the optimality rule for feature selection is independent of the classifier, which is used in the classifier design stage.
Data in practice are often limited, and the training sample has to be used for both designing the classifier and estimating the true error.
For comparisons of molecular subtype classifications, we applied a commonly used classifier designed for single sample predictions, the 50-gene subtype classifier (PAM50) developed by Parker et al. [ 18].
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